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Brazilian mean years of schooling

Source: R/get_schooling.R
get_schooling.Rd

Returns harmonized series of average years of schooling of the population aged 15 to 64, at the national, macro-region, or state level, optionally broken down by color/race or sex. The bundled data comes from Walter & Kang (2024, Economic History of Developing Regions; first circulated as a 2023 FGV-IBRE working paper), which reconstructs the series from 1925 to 2015 (states and regions from 1950).

Usage

get_schooling(
  year = NULL,
  geo_level = c("BR", "region", "UF"),
  geo = NULL,
  dimension = c("none", "race", "sex"),
  source = NULL,
  wide = FALSE,
  lang = c("en", "pt")
)

Arguments

year

Integer vector or two-element c(min, max) range. NULL for all years.

geo_level

One of "BR" (national, default), "region" (macro-region), or "UF" (state). Region and UF series start in 1950.

geo

Character vector of geographic codes. For geo_level = "UF", 2-letter IBGE UF abbreviations (e.g. "SP", "BA"). For geo_level = "region", one or more of "N", "NE", "CO", "SE", "S". NULL (default) returns all geographies at that level.

dimension

Inequality breakdown. One of:

  • "none" (default) — national totals only (no race or sex split);

  • "race" — breakdown by IBGE color/race (white, black, brown, asian, indigenous), totals across sex;

  • "sex" — breakdown by sex (male, female), totals across race. Race and sub-national breakdowns are only available at geo_level = "BR".

source

Character vector of source keys. NULL returns all available sources (currently only "walter_kang_2023"). The alias "walter_kang_2024" (year of the peer-reviewed article) is accepted and resolves to the same source.

wide

Logical. If TRUE, pivots the result to wide form. For this indicator the effect is minimal (only one indicator column), but the parameter is provided for API consistency with get_enrollment(). Default FALSE.

lang

One of "en" (default) or "pt". When "pt", factor levels are translated via inst/dict/i18n.yaml.

Value

A tibble following the canonical schema in inst/dict/schema.yaml. Columns: year, geo_level, geo_code, geo_name, dim_race, dim_sex, age_group, indicator, value, unit, source, source_note. The level and network columns are omitted (not applicable to population-level attainment averages).

Examples

# National series, all years
get_schooling()
#> # A tibble: 91 × 12
#>     year geo_level geo_code geo_name dim_race dim_sex age_group indicator  value
#>    <int> <chr>     <chr>    <chr>    <chr>    <chr>   <chr>     <chr>      <dbl>
#>  1  1925 BR        BR       Brasil   total    total   NA        mean_year…  1.13
#>  2  1926 BR        BR       Brasil   total    total   NA        mean_year…  1.15
#>  3  1927 BR        BR       Brasil   total    total   NA        mean_year…  1.16
#>  4  1928 BR        BR       Brasil   total    total   NA        mean_year…  1.18
#>  5  1929 BR        BR       Brasil   total    total   NA        mean_year…  1.2 
#>  6  1930 BR        BR       Brasil   total    total   NA        mean_year…  1.2 
#>  7  1931 BR        BR       Brasil   total    total   NA        mean_year…  1.21
#>  8  1932 BR        BR       Brasil   total    total   NA        mean_year…  1.23
#>  9  1933 BR        BR       Brasil   total    total   NA        mean_year…  1.24
#> 10  1934 BR        BR       Brasil   total    total   NA        mean_year…  1.26
#> # ℹ 81 more rows
#> # ℹ 3 more variables: unit <chr>, source <chr>, source_note <chr>

# By race, 1960-2015
get_schooling(dimension = "race", year = c(1960, 2015))
#> # A tibble: 224 × 12
#>     year geo_level geo_code geo_name dim_race dim_sex age_group indicator  value
#>    <int> <chr>     <chr>    <chr>    <chr>    <chr>   <chr>     <chr>      <dbl>
#>  1  1960 BR        BR       Brasil   asian    total   NA        mean_year…  3.52
#>  2  1961 BR        BR       Brasil   asian    total   NA        mean_year…  3.87
#>  3  1962 BR        BR       Brasil   asian    total   NA        mean_year…  4.19
#>  4  1963 BR        BR       Brasil   asian    total   NA        mean_year…  4.3 
#>  5  1964 BR        BR       Brasil   asian    total   NA        mean_year…  4.54
#>  6  1965 BR        BR       Brasil   asian    total   NA        mean_year…  4.81
#>  7  1966 BR        BR       Brasil   asian    total   NA        mean_year…  5.02
#>  8  1967 BR        BR       Brasil   asian    total   NA        mean_year…  5.23
#>  9  1968 BR        BR       Brasil   asian    total   NA        mean_year…  5.36
#> 10  1969 BR        BR       Brasil   asian    total   NA        mean_year…  5.55
#> # ℹ 214 more rows
#> # ℹ 3 more variables: unit <chr>, source <chr>, source_note <chr>

# By sex across states
get_schooling(dimension = "sex", geo_level = "UF", geo = c("SP", "BA"))
#> # A tibble: 0 × 12
#> # ℹ 12 variables: year <int>, geo_level <chr>, geo_code <chr>, geo_name <chr>,
#> #   dim_race <chr>, dim_sex <chr>, age_group <chr>, indicator <chr>,
#> #   value <dbl>, unit <chr>, source <chr>, source_note <chr>